The concept of audience interactivity has been rediscovered across many domains of storytelling and entertainment-e.g. digital games, in-person role-playing, film, theater performance, music, and theme parks-that enrich the form with new idioms, language, and practices. In this paper, we introduce a Spectrum of Audience Interactivity that establishes a common vocabulary for the design space across entertainment domains. Our spectrum expands on an early vocabulary conceptualized through co-design sessions for interactive musical performances. We conduct a cross-disciplinary literature review to evaluate and iterate upon this vocabulary, using our findings to develop our validated spectrum.

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Bolstered by a growing interest in simulating believable non-player characters (NPCs), work on NPC models has spanned topics such as planning, procedural storytelling, decision-making, and social dynamics. However, research groups work in isolation, designing and discussing their character models with disparate approaches, often using project-specific terminology. This makes it challenging to identify, classify, and accumulate existing knowledge. It is our position that since modelling of virtual characters has become an integral part of the scientific practice in our field, we must develop a common taxonomy to discuss these models. With this goal in mind, we conduct an in-depth analysis of a selection of projects, categorizing existing agent social interactions, and comparing results from research-based and commercial social simulation works in the entertainment domain. We conceptualize a taxonomy that classifies agent interactions by their social behaviours, inter-agent communication, knowledge flow, and the change in their relationships. We posit such a taxonomy would allow scientists to reproduce and evaluate existing models, collaborate on standards, share advances with other researchers and practitioners, allow for better communication and methodologies developed for new techniques, and allow for a more rigorous model-to-model analysis.

Creating believable simulations of large populations of characters in virtual worlds represents a grand challenge for interactive artificial intelligence, requiring reasoning about social intelligence. In this paper, we focus on one aspect of this challenge: the dynamics of opinion change for virtual characters and its relationship with social affinity. We developed a simulated population of characters that debate politically-charged topics, called Lyra. Characters’ knowledge, opinions, and biases spread through this society based on existing cognitive models and social science theories. Our simulation generates outlines of group conversations that portray the system’s evolution, and clusters characters into affinity groups based on the outcome of the debates. We conducted a human-subjects study to evaluate these generated conversations and affinity groups for their believability and to inform future iterations of the simulation. We believe successful simulation of opinion change in social dynamics provides a foundation for computational recognition, prediction, and interfacing with humans.

Social simulation research seeks to understand the dynamics of complex human behavior by simulating populations of individual decision-makers as multi-agent systems. However, prior work in games and entertainment fail to account for interactions between social behavior, geography, and relationships in a manner that allows researchers to easily reuse their frameworks and model social characters. We present Anthology, an extensible software framework for modeling human social systems, within the context of an ongoing research agenda to integrate AI techniques from social simulation games and computational social science to enable researchers to model and reason about the complex dynamics of human social behavior. Anthology comprises a motive-based agent decision making algorithm; a knowledge representation system for relationships; a flexible specification language for precondition-effect-style actions; a user interface to inspect and interact with the simulation as it runs in real-time; and an extensive user documentation and reference manual. We describe our participatory research design process used for the developing Anthology, the state of the current system, it's limitations and our future development directions.

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A live interactive narrative (LIN) is an experience where multiple players take on fictional roles and interact with real-world objects and actors to participate in a pre-authored narrative. Temporal properties of LINs are important to its viability and aesthetic quality and hence deserve special design consideration. In this paper, we tackle the largely overlooked problem of scheduling a multiplayer interactive narrative and propose the Live Interactive Narrative Scheduling Problem (LINSP), which handles reasoning under temporal uncertainty, resource scheduling, and non-linear plot choices. We present a mixed-integer linear programming formulation of the problem and empirically evaluates its scalability over large narrative instances.

The use of artificial intelligence and procedural content generation algorithms in mixed reality games is an unexplored space. We posit that these algorithms can enhance the gameplay experience in mixed reality games. We present two prototype games that use procedural content generation to design levels that make use of the affordances in the player’s physical environment. The levels produced can be tailored to a user, customizing gameplay difficulty and affecting how the player moves around the real-world environment.